Distributed Computation of Maximum Lifetime Spanning Subgraphs in Sensor Networks

Abstract

Abstract. We present a simple and efficient distributed method for determining the transmission power assignment that maximises the lifetime of a data-gathering wireless sensor network with stationary nodes and static power assignments. Our algorithm determines the transmission power level inducing the maximum-lifetime spanning subgraph of a network by means of a distributed breadth-first search for minmax-power communication paths, i.e. paths that connect a given reference node to each of the other nodes so that the maximum transmission power required on any link of the path is minimised. The performance of the resulting Maximum Lifetime Spanner (MLS) protocol is validated in a number of simulated networking scenarios. In particular, we study the performance of the protocol in terms of the number of required control messages, and compare it to the performance of a recently proposed Distributed Min-Max Tree (DMMT) algorithm. For all network scenarios we consider, MLS outperforms DMMT significantly. We also discuss bringing down the message complexity of our algorithm by initialising it with the Relative Neighbourhood Graph (RNG) of a transmission graph rather than the full graph, and present an efficient distributed method for reducing a given transmission graph to its RNG.

Energy-Efficient Bridge Detection Algorithms for Wireless Sensor Networks

“…From this property, BFS can be used to construct shortest path trees rooted at the sink node. BFS is used widely in sensor network for various purposes such routing and localization [12][13][14][15][16]. Besides, BFS can be modified to detect bridges.…”

Lifetime Maximization in Wireless Sensor Networks by Distributed Binary Search

“…Since their technique can easily be adapted also for the purpose of sensor network lifetime maximization, and seems to be the proposal in the literature closest to our BSpan approach, we conducted an experimental comparison of the runtime behavior of the algorithms DMMT, the recently proposed Maximum Lifetime Spanner (MLS) algorithm [7] and our proposed BSpan algorithm.…”

“…We experimentally evaluated the algorithm described in the previous section using the ns2 [5] network simulator and compared it to two previously proposed algorithms, Distributed Min-Max Tree (DMMT) [6] and Maximum Lifetime Spanner (MLS) [7]. To measure the performance of the algorithms, we considered both the number of control messages and the time it takes for the algorithms to finish.…”

“…The Maximum Lifetime Spanner (MLS) algorithm proposed in [7] uses a breadthfirst search approach to construct paths with minmax edge cost, which are combined to form a minmax power spanner of the transmission graph. Starting from the reference node, messages containing the lowest edge cost known so far are propagated in the network.…”

“…Section 4 describes our distributed method for finding a spanning tree of a given network with minimum maximum transmission cost. In Section 5 we evaluate our proposed BSpan algorithm in terms of the number of required control messages, and compare it to the performance of the Distributed Min-Max Tree algorithm proposed in [6] and the Maximum Lifetime Spanner (MLS) algorithm proposed in [7]. For our experimental comparison we use the ns2 network simulator.…”